Can a Prophet Inspire Change?

Background

Members of the Church of Jesus Christ of Latter-Day Saints believe the Prophet and President of the Church to be an inspired leader who is called of God. Through the history of the church generally each prophet has had a focus to which they devote their time and teachings.

Church members believe the Prophet is the only leader with the authority to lead the church as a whole, but to what extent can Prophets inspire change therein? This analysis will seek to prove three examples of significant changes within church culture each from a different Prophet.

Click the tabs below to view how President Hinckley changed the way the church talks about temples, how President Benson increased references to the Book of Mormon in General Conference, and how President Nelson has inspired more discussions on the “Gathering of Israel” than ever before!

Temples:

(“Temple”, “Temples”, “House of God”, “House of the Lord”)

Temple construction has boomed since 1995. From 1995 to 2020 the world went from 47 to 168 in operation. This is an unprecedented increase!

Of temples President Hinckley said, “We are determined, brethren, to take the temple to the people and afford them every opportunity for the very precious blessings that come of temple worship.” October 1997 Address

During his time as Prophet nearly 80 temples were dedicated - an impressive number as when he was called to the position there were less than 50 in operation. Through the design and construction of smaller temples, and an increase in the rate at which temples were announced and built, President Hinckley introduced the church to a new age of temple availability that continues to this day.

Clearly the number of temples in the world has increased, but has the culture of the church as a whole changed because of this focus? One way we can find out is by analyzing the number of references to temples in General Conference talks.

# Create a new column to count occurrences of the specified words
temple_data <- talks_data %>%
  mutate(
    temple_count = str_count(tolower(text), "\\btemple\\b") + str_count(tolower(text), "\\btemples\\b"),
    house_of_the_lord_count = str_count(tolower(text), "\\bhouse of the lord\\b"),
    house_of_god_count = str_count(tolower(text), "\\bhouse of god\\b")
  ) %>% 
  mutate(
    total_temple = temple_count + house_of_the_lord_count + house_of_god_count
  )

Scatter Plots

This plot shows there seems to be an increase in temple-related words following President Hinckley’s sustaining as prophet. The large gap in the middle is due to the sudden jump from 63 temples in 1999 to 102 temples in 2000. There are no dots through that section because no General Conference addresses were given in that time frame.

temple_by_year <- temple_data %>% 
  group_by(year) %>% 
  summarise(total = sum(total_temple)) %>% 
  mutate(temples = map_int(year, ~ sum(dedication_data$year <= .x)))

ggplot(data = temple_by_year) +
  geom_point(mapping = aes(x = temples, y = total), color = "forestgreen") +
  geom_vline(xintercept = 47, color = "red") +
  theme_bw() +
  labs(
    title = "Use of Temple Related Words in General Conference Talks",
    subtitle = "1995 Highlighted - Year President Gordon B. Hinckley became Church President",
    caption = '"Temple", "Temples", "House of God", "House of the Lord"',
    x = "Number of Dedicated Temples",
    y = "Mentions of the Temple"
  )

This plot shows the number of temples in the world by year:

graph <- ggplot(data = temple_by_year) +
  geom_point(mapping = aes(x = year, y = temples), color = "forestgreen", size = 1) +
  geom_line(mapping = aes(x = year, y = temples), color = "forestgreen") +
  theme_bw() +
  labs(
    title = "Number of Temples in the World by Year",
    subtitle = "Data is representing the count of dedicated temples at the end of the year",
    x = "Year",
    y = "Number of Dedicated Temples"
  ) 

graph

This time the phrases are shown in different colors.

temple_pivot <- temple_data %>% 
  select(year, speaker, title, temple_count, house_of_the_lord_count, house_of_god_count) %>% 
  pivot_longer(
    cols = c(temple_count, house_of_the_lord_count, house_of_god_count),
    names_to = "type",
    values_to = "count"
  ) %>% 
  group_by(year, type) %>% 
  summarise("total" = sum(count)) %>% 
  mutate("type_label" = case_when(
    type == "temple_count" ~ "Temples",
    type == "house_of_the_lord_count" ~ "House of the Lord",
    type == "house_of_god_count" ~ "House of God"
  ))

ggplot(data = temple_pivot) +
  geom_point(mapping = aes(x = year, y = total, color = type_label)) +
  geom_vline(xintercept = 1995, color = "red") +
  geom_smooth(method = lm, se = FALSE, mapping = aes(x = year, y = total, color = type_label)) +
  theme_bw() +
  labs(
    title = "Use of Temple Related Words in General Conference Talks",
    subtitle = "1995 Highlighted - Year President Gordon B. Hinckley became Church President",
    caption = '"Temple", "Temples", "House of God", "House of the Lord"',
    color = "Mention Type",
    x = "Year",
    y = "Mentions of the Temple"
  ) 

Box Plot

This boxplot shows that there is an increase in temple-related words between before President Hinckley was the prophet and our current Post-President Hinckley era. It seems temples are being talked about more now than they were before his sustaining.

temple_boxes <- temple_by_year %>% 
  mutate(group = case_when(
    year < 1995 ~ "Before Hinckley",
    year >= 1995 ~ "Post Hinckley"
  ))


ggplot(data = temple_boxes) + 
  geom_boxplot(mapping = aes(x = group, y = total)) + 
  theme_bw()

T Test

This T Test is a statistical test meant to tell us if the means of both populations differ from each other. Because our result (3.039e-06) is smaller than the confidence interval of 95% we can conclude the two means are significantly different.

On top of that, based on the box plots of the previous tab, it is easy to conclude that Temples are mentioned more after President Hinckley’s call than before he was the prophet!

t.test(total ~ group, data = temple_boxes, mu = 0, alternative = "two.sided", conf.level = 0.95) %>% 
  pander()

Welch Two Sample t-test: total by group (continued below)

Test statistic	df	P value	Alternative hypothesis
-5.255	50.19	3.039e-06 * * *	two.sided

mean in group Before Hinckley	mean in group Post Hinckley
107.1	177.6

Book of Mormon:

(“Book of Mormon”)

1986 was a big year for the Book of Mormon. President Ezra Taft Benson delivered a General Conference talk instructing members to use the Book of Mormon in teachings.

“This is a gift of greater value to mankind than even the many wonderful advances we have seen in modern medicine” October 1996 Address

How did this calling to action effect the number of quotes from the Book of Mormon in General Conference talks?

bom_data <- talks_data %>%
  mutate(
    total_bom = str_count(tolower(text), "(?i)\\bbook of mormon\\b")
  )

Scatter Plots

bom_by_year <- bom_data %>% 
  group_by(year) %>% 
  summarise(total = sum(total_bom))

ggplot(data = bom_by_year) +
  geom_point(mapping = aes(x = year, y = total), color = "forestgreen") +
  geom_vline(xintercept = 1986, color = "red") +
  theme_bw() +
  labs(
    title = "Use of Book of Mormon Related Words in General Conference Talks",
    subtitle = "1986 Highlighted - Year President Ezra Taft Benson Reaffirmed the Book of Mormon",
    caption = '"Book of Mormon"',
    x = "Year",
    y = "Mentions of the Book of Mormon"
  )

This scatter plot represents the number of times speakers used the term, “Book of Mormon” in their talks. There is an increase to be seen in the plot, but not a large one.

Are the scriptures from the book of mormon quoted more now than in the 70’s before the talk?

bom_quotes <- quotes %>% 
  filter(overall_book == "Book of Mormon") %>% 
  group_by(talk_year) %>% 
  summarise(total = n())

ggplot(data = bom_quotes, aes(x = talk_year, y = total)) + 
  geom_point(color = "red2") + 
  geom_smooth(method = lm, se = FALSE, color = "orange", linetype = "solid") +
  geom_vline(xintercept = 1986, color = "blue3") +
  theme_bw() +
  labs(
    title = "Quotes from the Book of Mormon in General Conference Talks",
    subtitle = "1986 Highlighted - Year President Ezra Taft Benson Reaffirmed the Book of Mormon",
    caption = '"Book of Mormon"',
    x = "Year",
    y = "Quotes from the Book of Mormon"
  )

This plot represents the number of scriptures quoted from Book of Mormon verses within General Conference talks per year. Interestingly this plot seems to have a correlation with the previous, in which case it too has a positive change following the message from President Benson.

The following tabs will attempt to prove, statistically, that this positive change is significant.

mylm <- lm(total ~ talk_year, data = bom_quotes)
summary(mylm) %>% 
  pander()

	Estimate	Std. Error	t value	Pr(>|t|)
(Intercept)	-2513	852.3	-2.948	0.004812
talk_year	1.35	0.4268	3.163	0.002627

Fitting linear model: total ~ talk_year

Observations	Residual Std. Error	\(R^2\)	Adjusted \(R^2\)
53	47.53	0.164	0.1476

Box Plot

bom_boxes <- bom_quotes %>% 
  mutate(group = case_when(
    talk_year < 1986 ~ "Before Talk", 
    talk_year >= 1986 ~ "Post Talk"
  ))

bom_means <- bom_boxes %>% 
  group_by(group) %>% 
  summarise(mean = mean(total))

ggplot(data = bom_boxes) + 
  geom_boxplot(mapping = aes(x = group, y = total)) + 
  geom_point(data = bom_means, mapping = aes(x = group, y = mean), color = "red3") +
  theme_bw()

These boxplots show a large change in the number of Book of Mormon quotes represented in General Conference talks between the two groups. Before President Benson reaffirmed the Book of Mormon the typical year would see around 140 qoutes from the book of mormon, and at most 170 quotes in a year.

However, in contrast, in the years following the prophet’s message we typically see around 180 quotes from the Book of Mormon in a year, with some years seeing over 300 quotes! This is a significant increase from the distribution before the message.

T Test

t.test(total ~ group, data = bom_boxes, mu = 0, alternative = "two.sided", conf.level = 0.95) %>% 
  pander()

Welch Two Sample t-test: total by group (continued below)

Test statistic	df	P value	Alternative hypothesis
-5.16	38.8	7.634e-06 * * *	two.sided

mean in group Before Talk	mean in group Post Talk
141.3	200

This T Test is a statistical test meant to tell us if the means of both populations differ from each other. Because our result (7.634e-06) is smaller than the confidence interval of 95% we can conclude the two means are significantly different.

From this result and the boxplots in the previous tab we can conclude that there are more quotes from the Book of Mormon in General Conference talks following the message from President Benson than before!

Bible Comparison

bible_compare <- quotes %>% 
  filter(overall_book %in% c("Book of Mormon", "Bible")) %>% 
  group_by(talk_year, overall_book) %>% 
  summarise(total = n())

ggplot(data  = bible_compare) + 
  geom_point(mapping = aes(x = talk_year, y = total, color = overall_book)) + 
  geom_smooth(se = FALSE, mapping = aes(x = talk_year, y = total, color = overall_book)) + 
  theme_bw()

Is there a difference in boxplots Pre President Benson and Post President Benson?

benson_check <- bible_compare %>% 
  mutate(group = case_when(
    talk_year < 1986 ~ "Before Talk",
    talk_year >= 1986 ~ "Post Talk"
  )) %>% 
  mutate(overall_group = paste(overall_book, group, sep = " - "))

ggplot(data = benson_check) + 
  geom_boxplot(mapping = aes(x = overall_group, y = total, fill = overall_book)) + 
  theme_bw()

Gathering Israel

(“Gathering Israel”, “Both Sides of the Veil”)

The main focus of President Nelson’s ministry is, “Gathering Israel on both sides of the veil”. This focus includes missionary work, temple work, and each individual’s commitment to following the covenant path.

Has this focus permeated the church as a whole? Here we have conducted a simple word analysis to determine if phrases related to gathering Israel have increased.

Scatter Plots

gathering_data <- talks_data %>%
  mutate(
    gathering1 = str_count(tolower(text), "(?i)\\bgathering of israel\\b"),
    gathering2 = str_count(tolower(text), "(?i)\\bgathering israel\\b"),
    both_sides = str_count(tolower(text), "(?i)\\bboth sides of the veil\\b")
  ) %>% 
  mutate(gathering = gathering1 + gathering2)
  
gathering_pivot <- gathering_data %>% 
  select(year, speaker, title, gathering, both_sides) %>% 
  pivot_longer(
    cols = c(gathering, both_sides),
    names_to = "type",
    values_to = "count"
  ) %>% 
  group_by(year, type) %>% 
  summarise("total" = sum(count)) %>% 
  mutate(group = case_when(
    year < 2017 ~ "Before Nelson",
    year >= 2017 ~ "Post Nelson"
  ))

ggplot(data = gathering_pivot) +
  geom_point(mapping = aes(x = year, y = total, color = type)) +
  geom_smooth(se = FALSE, mapping = aes(x = year, y = total, color = type)) + 
  geom_vline(xintercept = 2017, color = "red2") +
  theme_bw() +
  labs(
    title = "Use of Gathering Israel Related Words in General Conference Talks",
    subtitle = "2017 Highlighted - President Nelson sustained as Prophet",
    caption = '"Gathering Israel", "Both Sides of the Veil"',
    color = "Mention Type",
    x = "Year",
    y = "Mentions of Gathering Israel"
  )

Based on this scatter plot we can see a major increase in words related to the gathering of Israel. In fact, the points before President Nelson became the prophet and the points following are entirely different!

Our statistical models have shown that this difference is significant! Therefore, we can safely conclude that President Nelson’s focus on the gathering of Israel has increase the mentions of the gathering in general conference talks.

Box Plot

gathering_means <- gathering_pivot %>% 
  group_by(group) %>% 
  summarise(mean = mean(total))

ggplot(data = gathering_pivot) + 
  geom_boxplot(mapping = aes(x = group, y = total)) + 
  geom_point(data = gathering_means, mapping = aes(x = group, y = mean), color = "red3") +
  theme_bw()

These boxplots tell quite a story. The median number of gathering of Israel mentions before President Nelson is nearly zero, and there are five points representing years with three or more references which are represented as outliers.

The median number of mentions after President Nelson’s call to be the prophet is now around eight mentions per year! The red dots on the plots represent the means of the groups, which have had quite a large jump between the two groups.

T Test

t.test(total ~ group, data = gathering_pivot, mu = 0, alternative = "two.sided", conf.level = 0.95) %>% 
  pander()

Welch Two Sample t-test: total by group (continued below)

Test statistic	df	P value	Alternative hypothesis
-6.129	13.57	2.997e-05 * * *	two.sided

mean in group Before Nelson	mean in group Post Nelson
0.8696	8.286

This T Test is a statistical test meant to tell us if the means of both populations differ from each other. Because our result (2.997e-05) is smaller than the confidence interval of 95% we can conclude the two means are significantly different.

From this result and the boxplots in the previous tab we can conclude that mentions of the Gathering of Israel in General Conference talks are higher following President Nelson’s call than before!

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